A310
Course Coordinator: 
Erik De Schutter
Computational Neuroscience
Description: 

Explore topics in computational neuroscience, from single neuron properties to networks of integrate-and-fire neurons. Review the biophysical properties of neurons and extend these findings to cable theory and passive dendrite simulations. Study excitability based on the Hodgkin-Huxley model of the action potential and the contributions of various other ion channels. Review phase space analysis, reaction-diffusion modeling and simulating calcium dynamics. Model single neurons, neuronal populations, and networks using NEURON software. Discuss seminal papers associated with each topic, and produce reports on modeling exercises.

Aim: 
This course introduces basic concepts and methods of computational neuroscience based on theory and a sampling of important scientific papers.
Course Content: 
  1. Introduction and the NEURON simulator
  2. Basic concepts and the membrane equation
  3. Linear cable theory
  4. Passive dendrites
  5. Modeling exercises 1
  6. Synapses and passive synaptic integration
  7. Ion channels and the Hodgkin-Huxley model
  8. Neuronal excitability and phase space analysis
  9. Other ion channels
  10. Modeling exercises 2
  11. Reaction-diffusion modeling and calcium dynamics
  12. Nonlinear and adaptive integrate-and-fire neurons
  13. Neuronal populations and network modeling
  14. Synaptic plasticity and learning
Course Type: 
Elective
Credits: 
2
Assessment: 
Active participation to textbook discussions in class (40%), reports on modeling papers (40%), written exercises (20%).
Text Book: 
  • Biophysics of Computation, by Christof Koch (1999) Oxford Press
  • Neural Dynamics: From Single Neurons to Networks and Models of Cognition, by Wulram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski (Cambridge University Press 2014)
Reference Book: 
  • Computational Modeling Methods for Neuroscientists, edited by Erik De Schutter (MIT Press 2010)
Prior Knowledge: 
Requires introductory neuroscience course or equivalent with background knowledge in computational methods, programming, mathematics.
Notes: